Meena E. Girgis

This paper shows the ability of Fractional-Order (FO) dynamics in adaptation laws of indirect adaptive fuzzy logic control (AFLC). The parameters of the FO-adaptive laws are optimized using Particle Swarm Optimization algorithm without the presence of the disturbance. The optimal FO-AFLC (OFO-AFLC) is introduced in this paper to overcome the drawbacks of AFLC. The complexity in AFLC is due to using the projection algorithm to keep the adapted parameters bounded. Also the AFLC uses a complex control to guarantee the error convergence. It has proven that the OFO-AFLC can guarantee the error convergence without using neither the projection algorithm nor complex control. The applicability and efficiency of the proposed method is compared to the ordinary AFLC, and demonstrated through simulations made on inverted pendulum model. Furthermore, simulation results show that the control signal in OFO-AFLC is smoother with less oscillations.

Nuclear fusion is a source of safe and clean energy that motivates researchers to enhance the control strategies for better performance. In parallel, incorporating the fractional-order calculus into control strategies improves the system performance and robustness with less control effort. In this paper, optimal fractional -order proportional-integral-derivative (OFO-PID) controller is developed to control the plasma shape, position and current in Tokamaks. The OFO-PID parameters, gains and fractional-orders, are tuned using particle swarm optimization (PSO) algorithm. The main objective of the controller is to improve the magnetic system performance of TCV (tokamak a` configuration variable) through guaranteeing high tracking performance. Moreover, the controller has to reduce the coupling effect between the five outputs of the system without violating the coils’ voltage physical constraints in the presence of the disturbance. The tokamak simulation results show the superiority of the proposed OFO-PID over other control techniques regarding the tracking and decoupling performance.

A multi-core processor is an Integrated Circuit (IC) which two or more processors have been attached inside a single package for enhanced performance, reduced power consumption, and more efficient simultaneous processing of multiple tasks. [n fact, the chip power consumption is limited by cooling system level capacity. The air cooling limitation is already reached in 2006. The CPU reaches the maximum operational temperature after certain time due to maximum CPU utilization, thus the CPU utilization is reduced to the safe utilization in order not to exceed the power limit, and this phenomenon is called CPU thermal throttling. The cores thermal problem is managed by adding temperature constraints through a threshold temperature (maximum temperature). This problem is related to the power consumption management that is a function of the operating frequency of each core where damaging one core means damaging the whole chip. The current software solutions of thermal management problems are not effective as they are slow and don' t take into the consideration the mutual heat transfer between the cores i.e. the correlation. Therefore, a hardware solution is proposed in this paper that takes into consideration the mutual heat transfer between the cores. This solution is built using the three dimensional fuzzy logic controller. The obtained results prove that the 3-D fuzzy logic controller is able to process the multicore CPU correlation information to improve the thermal management response and reduce the air cooling limitation effect. The controller is implemented using FPGAs technology where different architectures are explored according to the given constraints.